{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../dataset/metadata/test/test.csv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"duration\"] = df[\"offset\"] - df [\"onset\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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aK3k/Drgb+EvgGYtsumiv+r2ZY3eY44Zt58wsu82+WuvHbUvpUxaY7DyjnPNI0nj4IZov3Y8m2QYcDdyc5Ps7TSVJkiQN9nTgn6rqy1X1HeBK4CeAw9phbNAc197RVUBpklg8kta4qrq1qh5TVeuqah1N996ntPOWSZIkSX10O/C0JAenmXdhbo7dG4DntNs4x660QiweSWtMkkuBDwDHJdnezj8mSZIkjY32qt5XADcDt9L8bbsJeAXw8iSfBR4FXNxZSGmCjHrCbEk9U1VnL3P/ulWKIkmSJO2zqno18OoFq28DTuogjjTR7HkkSZIkSZKkgSweSZIkSZIkaSCLR5IkSZIkSRrI4pEkSZIkSZIGsngkSZIkSZKkgSweSZIkSZIkaSCLR5IkSZIkSRrI4pEkSZIkSZIGsngkSVIPJDkmyQ1Jtib5RJIL2vWvSfLFJLe0P8/sOqskSZLWlgO7DiBJkgDYDWyoqpuTHArclOS69r43VtUfdphNkiRJa5jFI0mSeqCqdgA72tv3JtkKHNVtKkmSJMnikSRJvZNkHfBk4EbgZOAlSX4Z+DBN76S7FnnMemA9wNTUFLOzswP3v+H43ctmWOrxK2HXrl0jf46VYtbRGbe8kiStVRaPJEnqkSQPA94JvKyq7knyx8DvAtX+fj3wooWPq6pNwCaA6enpmpmZGfgc5228Ztkc284Z/PiVMDs7y1IZ+8SsozNueSVJWqucMFuSpJ5I8mCawtElVXUlQFXdWVX3VdV3gf8BnNRlRkmSJK09Fo8kSeqBJAEuBrZW1RvmrT9y3mY/B3x8tbNJkiRpbXPYmiRJ/XAy8ALg1iS3tOteBZyd5ESaYWvbgBd3E0+SJElrlcUjSZJ6oKreD2SRu/56tbNIkiRJ8zlsTZIkSZIkSQNZPJIkSZIkSdJAFo8kSZIkSZI0kHMeSZKkPazbeM2y22y78FmrkESSpD0lOQ54x7xVjwd+C/jzdv06mgtNPK+q7lrtfNKkseeRJEmSJGmsVNWnq+rEqjoReCrwTeAqYCNwfVUdC1zfLkvaTxaPJEmSJEnj7FTgc1X1eeBMYEu7fgtwVmeppAnisDVJkiRpCUm2AfcC9wG7q2o6yeE4NEbqi+cDl7a3p6pqB0BV7UjymMUekGQ9sB5gamqK2dnZ++/btWvXA5Y3HL972QDzt19pC/N0rU95+pQFJjuPxSNJkiRpeT9dVV+Ztzw3NObCJBvb5Vd0E01au5I8BHg28Mq9eVxVbQI2AUxPT9fMzMz9983OzjJ/+bxh5gE8Z2bZbfbVwjxd61OePmWByc7jsDVpjUny1iQ7k3x83ro/SPKpJB9LclWSw7rMKEnSGHBojNQPzwBurqo72+U7kxwJ0P7e2VkyaYLY80haezYDf0RzJYo51wGvrKrdSV5Hc+bGs6eSJDUKuDZJAX/a9ljY76ExC3U9NGZYfRuWsdJ8fWPnbL43ZA3gauBc4ML297u6CCVNmpEWjxwfLvVPVb0vyboF666dt/hB4DmrmUmSpJ47uaruaAtE1yX51LAPXGpozEJdD40ZVt+GZaw0X9/4SHIwcBrw4nmrLwQuT3I+cDvw3C6ySZNmNXoeOT5cGi8voinwLmrQGdS+ncUyz9LWep5hzu737T2S1J2quqP9vTPJVcBJtENj2l5HDo2ROlBV3wQetWDdV2muviZpBXUxbO1MYKa9vQWYxeKR1AtJfhPYDVwyaJtBZ1D7dhbLPEtb63mGObu/+fRDevUeSepGkkOAB1XVve3tnwF+B4fGSJLWkFEXj0YyPnyxs8HLnUX20ondMc/S+pInybnAGcCpVVVd55EkqSemgKuSQHPs/Paqem+SD+HQGEnSGjHq4tFIxocvdoZ6ubPIXjqxO+ZZWh/yJDmdpgfgv2m7/0qSJKCqbgNOWGS9Q2MkSWvGg0a58/njw4EHjA8HL50odSHJpcAHgOOSbG/PmP4RcChNkfeWJH/SaUhJkiRJUm+MrOeR48OlfqqqsxdZffGqB5EkSZIkjYVRDltzfLgkSZIkSdKYG1nxyPHhkiRJkiRJ42+kcx5JkiRJkiRpvFk8kiRJkiRJ0kAWjyRJkiRJkjSQxSNJkiRJkiQNZPFIkiRJkiRJA1k8kiRJkiRJ0kAWjyRJkiRJkjSQxSNJkiRJkiQNZPFIkiRJkiRJA1k8kiRJkiRJ0kAWjyRJkiRJkjSQxSNJkiRJkiQNZPFIkiRJkiRJA1k8kiRJkiRJ0kAWjyRJkiRJYyfJYUmuSPKpJFuT/HiSw5Ncl+Qz7e9Hdp1TmgQWjyRJkiRJ4+gi4L1V9SPACcBWYCNwfVUdC1zfLkvaTxaPJEnqgSTHJLmhPXP6iSQXtOs9gypJ0gJJHg78FHAxQFV9u6ruBs4EtrSbbQHO6iahNFkO7DqAJEkCYDewoapuTnIocFOS64DzaM6gXphkI80Z1Fd0mFOSpD54PPBl4G1JTgBuAi4ApqpqB0BV7UjymMUenGQ9sB5gamqK2dnZ++/btWvXA5Y3HL972TDzt19pC/N0rU95+pQFJjuPxSNJknqgPdCdO9i9N8lW4CiaM6gz7WZbgFksHkmSdCDwFOClVXVjkovYiyFqVbUJ2AQwPT1dMzMz9983OzvL/OXzNl6z7P62nTOz7Db7amGervUpT5+ywGTnsXgkSVLPJFkHPBm4kRU4g7rQMGdQh7E/Z7L6dmZuKWYdnXHLK6lXtgPbq+rGdvkKmuLRnUmObL8zjwR2dpZQmiAWjyRJ6pEkDwPeCbysqu5JMtTjljqDutAwZ1CHsT9nWft2Zm4pZh2dccsrqT+q6ktJvpDkuKr6NHAq8Mn251zgwvb3uzqMKU0Mi0eSJPVEkgfTFI4uqaor29WeQZUkaXEvBS5J8hDgNuCFNBeFujzJ+cDtwHM7zCdNDItH0hqT5K3AGcDOqnpSu+5w4B3AOmAb8LyququrjNJalKaL0cXA1qp6w7y7rsYzqJIk7aGqbgGmF7nr1NXOIk26B3UdQNKq2wycvmDdRpqrOR0LXM9eTDYoacWcDLwAOCXJLe3PM2mKRqcl+QxwWrssSZIkrRp7HklrTFW9r52Mdz6v5iR1rKreDwya4MgzqJIkSeqMxSNJMOTVnGDwFZ36dsUc8yxtrecZ5mpjfXuPJEmSpK5YPJK0VwZd0alvV8wxz9LWep5hrja2+fRDevUeSZIkSV1xziNJ0F7NCcCrOUmSJEmS5rN4JAm+dzUn8GpOkiTtIckBST6S5D3t8uOS3JjkM0ne0V4qXJKkiWTxSFpjklwKfAA4Lsn2JOfj1ZwkSVrOBcDWecuvA97YXqn0LuD8TlJJkrQKnPNIWmOq6uwBd3k1J0mSFpHkaOBZwGuBlycJcArwi+0mW4DXAH/cSUBJkkZs5MWjJAcAHwa+WFVnJHkccBlwOHAz8IKq+vaoc0iSJEn76E3AbwCHtsuPAu6uqrlLN24HjlrsgYOuUrqYYa4E2YerQE761Sh9fZK0p9XoeTTXxffh7fJcF9/LkvwJTRdfz9JIkiSpd5KcAeysqpuSzMytXmTTWuzxg65SuphhrgS57ZzBj18tfbti50rz9UnSnkY659G8Lr5/1i7PdfG9ot1kC3DWKDNIkiRJ++Fk4NlJttH0nj+FpifSYUnmTsQeDdzRTTxJkkZv1D2PRtLFd7Gulst18x1l18y+df00z9LMI0mShlVVrwReCdD2PPr1qjonyV8Cz6EpKHmlUknSRBtZ8WiUXXwX62q5XDffUXbx7VvXT/MszTySJGkFvAK4LMnvAR8BLu44jyRJIzPKnkdzXXyfCTyUZs6j+7v4tr2P7OIrSdKYWjfM/CwXPmsVkkiro6pmgdn29m3ASV3mkSRptYxszqOqemVVHV1V64DnA39fVecAN9B08QW7+EqSJEmSJPXaSCfMHuAVwMuTfJZmDiS7+EqSJEmSJPXUqCfMBuziK0mSJEmSNK666HkkSZIkSZKkMWHxSJIkSZIkSQNZPJIkSZIkSdJAqzLnkSRJkiRJKynJNuBe4D5gd1VNJzkceAewDtgGPK+q7uoqozQp7HkkSZIkSRpXP11VJ1bVdLu8Ebi+qo4Frm+XJe0ni0eSJEmSpElxJrClvb0FOKvDLNLEcNiaJEmSJGkcFXBtkgL+tKo2AVNVtQOgqnYkecxiD0yyHlgPMDU1xezs7P337dq16wHLG47fvWyQ+duvtIV5utanPH3KApOdx+KRJEmSJGkcnVxVd7QFouuSfGrYB7aFpk0A09PTNTMzc/99s7OzzF8+b+M1y+5v2zkzy26zrxbm6Vqf8vQpC0x2HoetSZIkSZLGTlXd0f7eCVwFnATcmeRIgPb3zu4SSpPD4pEkSZIkaawkOSTJoXO3gZ8BPg5cDZzbbnYu8K5uEkqTxWFrkiRJkqRxMwVclQSav2vfXlXvTfIh4PIk5wO3A8/tMKM0MSweSZIkSZLGSlXdBpywyPqvAqeufiJpsjlsTZIkSZIkSQNZPJIkSZIkSdJAFo8kSZIkSZI0kMUjSZIkSZIkDTRU8SjJBUkensbFSW5O8jOjDidpdSX5tSSfSPLxJJcmeWjXmSRJkiRJ3Rq259GLquoe4GeAI4AXAheOLJWkVZfkKOBXgemqehJwAPD8blNJkiRJkro2bPEo7e9nAm+rqo/OWydpchwIHJTkQOBg4I6O80iSJEmSOnbgkNvdlORa4HHAK5McCnx3dLEkrbaq+mKSPwRuB/4ZuLaqrl24XZL1wHqAqakpZmdnAdi1a9f9t/vAPEtb63k2HL972W369h5JkiRJXRm2eHQ+cCJwW1V9M8mjaIauSZoQSR4JnElTJL4b+Mskv1RVfzF/u6raBGwCmJ6erpmZGQBmZ2eZu90H5lnaWs9z3sZrlt1m8+mHrGqmJG8FzgB2tkNHSfIa4N8DX243e1VV/fWqhZIkSZIYftjadVV1c1XdDVBVXwXeOLpYkjrwdOCfqurLVfUd4ErgJzrOJK0lm4HTF1n/xqo6sf2xcCRJkqRVt2TPo/ZKSwcDj257JczNc/Rw4AdGnE3S6rodeFqSg2mGrZ0KfLjbSNLaUVXvS7Ku6xySJEnSQssNW3sx8DKaQtFNfK94dA/wlhHmkrTKqurGJFcANwO7gY/QDk+T1KmXJPllmmLuhqq6a7GNBs1Htphh5nxaKYNyjNOcUmYdnXHLK0nSWrVk8aiqLgIuSvLSqnrzKmWS1JGqejXw6q5zSLrfHwO/C1T7+/XAixbbcNB8ZIsZZs6nlbLtnMVz9G3eraWYdXTGLa8kSWvVUBNmV9Wbk/wEsG7+Y6rqz0eUS5KkNa+q7py7neR/AO/pMI4kSZLWqKGKR0n+J/BDwC3Afe3qAiweSZI0IkmOrKod7eLPAR/vMo8kSZLWpqGKR8A08MSqqlGGkSRprUpyKTBDc5GK7TRDSGeSnEhzwmYbzVyEkiRJ0qoatnj0ceD7gR3LbShJkvZeVZ29yOqLVz2IJEmStMCwxaNHA59M8o/At+ZWVtWzR5JKkiRJ6oEkDwXeB3wfzbHzFVX16iSPAy4DDqe5UukLqurb3SWVJGl0hi0evWaUISRJkqSe+hZwSlXtSvJg4P1J/gZ4OfDGqrosyZ8A59NcIVGSpIkz7NXW/tfe7tizNJIkSRp37Zyfu9rFB7c/BZwC/GK7fgvNyVaLR5KkiTTs1dbupfmSBHgIzZfmN6rq4Us8zLM0kiRJGntJDgBuAn4YeAvwOeDuqtrdbrIdOGrAY9cD6wGmpqaYnZ0d+Dwbjt898L45Sz1+tezatasXOUbF1ydJexq259Gh85eTnAWctMxjPEsjSZKksVdV9wEnJjkMuAp4wmKbDXjsJmATwPT0dM3MzAx8nvM2XrNslm3nDH78apmdnWWp1zHufH3joy3sfhj4YlWd4SgXaXSGnfPoAarqr5JsXG67UZ2lWaxavtyZmlFW1/tWvTfP0swjSZL2RVXdnWQWeBpwWJID2+Pao4E7Og0nrU0XAFuBuRExr8NRLtJIDDts7efnLT4ImGbA2ZX5RnWWZrFq+XJnakZ5lqZv1XvzLM08kiRpWEmOAL7TFo4OAp5O8wfqDcBzaHo5nAu8q7uU0tqT5GjgWcBrgZcnCY5ykUZsZwmuAAAgAElEQVRm2J5HPzvv9m5gG3DmsE/iWRpJkiSNqSOBLW2P+gcBl1fVe5J8Ergsye8BHwEu7jKktAa9CfgNYG6KlUcx5CgX2LuRLl3PR9a3kQp9ytOnLDDZeYad8+iFe7tjz9JIkiRp3FXVx4AnL7L+NpaZA1TSaCQ5A9hZVTclmZlbvcimA0fL7M1Il67nI+vbSIU+5elTFpjsPA8aZqMkRye5KsnOJHcmeWfbTXApRwI3JPkY8CHguqp6D/AKmm6Fn6WpDnuWRpIkSZI0rJOBZyfZRtMp4RSankiHJZnrIOEoF2kFDTts7W3A24Hntsu/1K47bdADPEsjSZIkSVppVfVK4JUAbc+jX6+qc5L8JY5ykUZiqJ5HwBFV9baq2t3+bAaOGGEuSZIkSZL2hqNcpBEZtufRV5L8EnBpu3w28NXRRJIkSZIkaXlVNQvMtrcd5SKNyLA9j14EPA/4ErCDpivgXk+iLUmSJEmSpPEybM+j3wXOraq7AJIcDvwhTVFJkiRJkiRJE2rYnkc/Olc4Aqiqr7HIZNiSJEmSJEmaLMMWjx6U5JFzC23Po2F7LUmSJEmSJGlMDVsAej3wv5NcARTN/EevHVkqSZIkSZIk9cJQxaOq+vMkHwZOAQL8fFV9cqTJJEmSJEmS1Lmhh561xSILRpIkSZIkSWvIsHMeSVoDkhyW5Iokn0qyNcmPd51JkiRJktQtJ72WNN9FwHur6jlJHgIc3HUgSZIkSVK3LB5JAiDJw4GfAs4DqKpvA9/uMpMkSZIkqXsWjyTNeTzwZeBtSU4AbgIuqKpvzN8oyXpgPcDU1BSzs7MA7Nq16/7bfWCepa31PBuO373sNn17jyRJkqSuWDySNOdA4CnAS6vqxiQXARuB/zJ/o6raBGwCmJ6erpmZGQBmZ2eZu90H5lnaWs9z3sZrlt1m8+mH9Oo9kiRJkrrihNmS5mwHtlfVje3yFTTFJEmSJEnSGmbxSBIAVfUl4AtJjmtXnQp8ssNIkiRJkqQecNiapPleClzSXmntNuCFHeeRJEmSJHXM4pGk+1XVLcB01zkkSZIkSf3hsDVJkiRJkiQNZPFIkiRJkiRJA1k8kiRJkiRJ0kAWjyRJkiRJkjSQxSNJkiRJ0lhJ8tAk/5jko0k+keS32/WPS3Jjks8keUd7FWFJ+8nikSRJPZDkrUl2Jvn4vHWHJ7muPQC+Lskju8woSVKPfAs4papOAE4ETk/yNOB1wBur6ljgLuD8DjNKE8PikSRJ/bAZOH3Buo3A9e0B8PXtsiRJa141drWLD25/CjgFuKJdvwU4q4N40sQ5sOsAkiQJqup9SdYtWH0mMNPe3gLMAq9YtVCSJPVYkgOAm4AfBt4CfA64u6p2t5tsB44a8Nj1wHqAqakpZmdn779v165dD1jecPxuljN/+5W2ME/X+pSnT1lgsvNYPJIkqb+mqmoHQFXtSPKYQRsudRC80DAHwStlUI65g5lbv/j1Zfdx/FGPWOFUe6dvB4JLGaesMH55JfVLVd0HnJjkMOAq4AmLbTbgsZuATQDT09M1MzNz/32zs7PMXz5v4zXLZtl2zsyy2+yrhXm61qc8fcoCk53H4pEkSRNgqYPghYY5CF4pgw6m5w5muj4gH0bfDgSXMk5ZYfzySuqnqro7ySzwNOCwJAe2vY+OBu7oNJw0IZzzSJKk/rozyZEA7e+dHeeRJKkXkhzR9jgiyUHA04GtwA3Ac9rNzgXe1U1CabJYPJIkqb+upjnwBQ+ApU4kOSbJDUm2tpcDv6Bd79UQpW4dCdyQ5GPAh4Drquo9NHMDvjzJZ4FHARd3mFGaGA5bkySpB5JcSjM59qOTbAdeDVwIXJ7kfOB24LndJZTWrN3Ahqq6OcmhwE1JrgPOo7ka4oVJNtJcDdEJ7aVVUlUfA568yPrbgJNWP5E02UZWPEpyDPDnwPcD3wU2VdVFSQ4H3gGsA7YBz6uqu0aVQ5KkcVBVZw+469RVDSLpAdpJ6+cmrr83yVaaqzd5NURJ0poxyp5HnqWRJEnSxEiyjqanw40MeTXElb4S4psvWZnRq/tzFcNJv0qer0+S9jSy4pFnaSRJkjQpkjwMeCfwsqq6J8lQjxu3KyEOY9Kvkufrk6Q9rcqcRyt9lmaxavlyZ2pGWV3vW/XePEszjyRJ2htJHkxTOLqkqq5sV9+Z5Mj2eNarIUqSJtrIi0ejOEuzWLV8uTM1+3N2ZTl9q96bZ2nmkSRJw0pz8HoxsLWq3jDvrrmrIV6IV0OUJE24B41y50udpWnv9yyNJEmS+uxk4AXAKUluaX+eSVM0Oi3JZ4DT2mVJkibSKK+25lkaSZIkjbWqej8wqOu8V0OUJK0Joxy2NneW5tYkt7TrXkVTNLo8yfnA7cBzR5hBkiRJkiRJ+2GUV1vzLI0kSZIkSdKYG+mcR5IkSZIkSRpvFo8kSZIkSZI0kMUjSZIkSZIkDWTxSNIDJDkgyUeSvKfrLJIkSZKk7lk8krTQBcDWrkNIkiRJkvrB4pGk+yU5GngW8GddZ5EkSZIk9cOBXQeQ1CtvAn4DOHTQBknWA+sBpqammJ2dBWDXrl33357v1i9+fcknPP6oR+xr1iUNytMV8yxttfNsOH73stv07T2SJEmSumLxSBIASc4AdlbVTUlmBm1XVZuATQDT09M1M9NsOjs7y9zt+c7beM2Sz7vtnIFPtV8G5emKeZa22nmW+38JsPn0Q3r1HkmSJEldcdiapDknA89Osg24DDglyV90G0mSJEmS1DWLR5IAqKpXVtXRVbUOeD7w91X1Sx3HkiRJkiR1zOKRJEmSJEmSBrJ4JGkPVTVbVWd0nUOSJElaTJJjktyQZGuSTyS5oF1/eJLrknym/f3IrrNKk8DikSRJkiRp3OwGNlTVE4CnAb+S5InARuD6qjoWuL5dlrSfLB5JkiRJksZKVe2oqpvb2/cCW4GjgDOBLe1mW4CzukkoTZYDuw4gSZIkSdK+SrIOeDJwIzBVVTugKTAlecyAx6wH1gNMTU0xOzt7/327du16wPKG43cvm2H+9ittYZ6u9SlPn7LAZOexeCRJkiRJGktJHga8E3hZVd2TZKjHVdUmYBPA9PR0zczM3H/f7Ows85fP23jNsvvbds7Mstvsq4V5utanPH3KApOdx2FrkiRJkqSxk+TBNIWjS6rqynb1nUmObO8/EtjZVT5pklg8kiRJkiSNlTRdjC4GtlbVG+bddTVwbnv7XOBdq51NmkQOW5MkSZIkjZuTgRcAtya5pV33KuBC4PIk5wO3A8/tKJ80USweSZIkSZLGSlW9Hxg0wdGpq5lFWgssHkmSJEmStB/WDTOp9oXPWoUk0mhYPJIkSSMz6GB6w/G7h7p6jSRJkrrnhNmSJEmSJEkayJ5HknpvpboBL7efldjHamaRJEmSpNVgzyNJkiRJkiQNZPFIkiRJkiRJA1k8kiRJkiRJ0kDOeSRJUs8l2QbcC9wH7K6q6W4TSZIkaS2xeCRJ0nj46ar6StchJEmStPasmeLRSl0haSWey6soSZIkSZKkcTGy4lGStwJnADur6kntusOBdwDrgG3A86rqrlFlkCRpQhRwbZIC/rSqNi3cIMl6YD3A1NQUs7OzA3e24fjdI4o5vKmDhs+x1GtZDbt27eo8w7DGKSuMR16PaSVJGm3Po83AHwF/Pm/dRuD6qrowycZ2+RUjzCBJ0iQ4uaruSPIY4Lokn6qq983foC0obQKYnp6umZmZgTs7b4jeuKO24fjdvP7W4Q5Dtp0zM9owy5idnWWp97NPxikrjE3ezXhMK0la40Z2tbX2oPZrC1afCWxpb28BzhrV80uSNCmq6o72907gKuCkbhNJa4fHtJIkrf6cR1NVtQOgqna0Z1AXtVT3+8W6OK9EF/x97Ta9MM9yWUbdPbtvXcDNs7S+5ZHUL0kOAR5UVfe2t38G+J2OY0lr3Yoc0y60mkNK9+fYY9KPXXx9krSn3k6YvVT3+8W6OK9EF/x97Ra/MM9yWUbd/b5vXcDNs7S+5ZHUO1PAVUmg+d5+e1W9t9tIkobV1yGl+3M8OunHLr4+SdrTaheP7kxyZHuG5khg5yo/vyRJY6WqbgNO6DqHpAfwmFaStKaMbM6jAa4Gzm1vnwu8a5WfX5IkSdpfHtNKktaUkRWPklwKfAA4Lsn2JOcDFwKnJfkMcFq7LKkHkhyT5IYkW5N8IskFXWeSJKlrHtNKkjTCYWtVdfaAu04d1XNK2i+7gQ1VdXOSQ4GbklxXVZ/sOpiktW3dEPPAbLvwWauQRGuRx7SSJK3+sDVJPVVVO6rq5vb2vcBW4KhuU0mSJEmSutbbq61J6k6SdcCTgRsXuW/RSw4PuuzrcpcdfvMly08TseH4ZTfZYz9TB+25brn9DHPZ2mEuo7zYfha+P8vtZ9SX0O3bZXpXO88w/459e48kSZKkrlg8msdu8RIkeRjwTuBlVXXPwvsHXXJ40GVfV/Oyw/NtOH43r7917z7ihrls8TCvZ7H9LHx/ltvP/lxCeRh9u0zvaucZ5t9x8+mH9Oo9kqSVtNxxr8e86rskbwXOAHZW1ZPadYcD7wDWAduA51XVXV1llCaJw9Yk3S/Jg2kKR5dU1ZVd55EkSZIG2AycvmDdRuD6qjoWuL5dlrQCLB5JAiBJgIuBrVX1hq7zSJIkSYNU1fuAry1YfSawpb29BThrVUNJE8xha5LmnAy8ALg1yS3tuldV1V93mEmSJEka1lRV7YDmYjBJHjNow0HzeMLez1U5rGHm+jz+qEfssa5v8zD2KU+fssBk57F4JAmAqno/kK5zSJIkSaM2aB5P2Pu5KlfSMHNndq1PefqUBSY7j8PWJEmSJEmT4M4kRwK0v3d2nEeaGPY8kiRJYvDVpzYcv/v+s85egUqSeu1q4Fzgwvb38uPEJA3FnkeSJEmSpLGS5FLgA8BxSbYnOZ+maHRaks8Ap7XLklaAPY8kSZIkSWOlqs4ecNepqxpEWiPseSRJkiRJkqSBLB5JkiRJkiRpIItHkiRJkiRJGsg5j/bSYldimX8Vln3dx0JezUWSJEldWep4de7Y1+NVaeUt9/em7U5dseeRJEmSJEmSBrJ4JEmSJEmSpIEctiZJrWGGlO7rfkYxvHV/7E33Z4faahz4/1SSJGl0LB5JkiRJ2msWbSVp7XDYmiRJkiRJkgayeCRJkiRJkqSBLB5JkiRJkiRpIOc8GmODxpnvzUS4+/M88zmefXHDvHebTz9kFZJIkiRJkrRvLB5JkiRJkjQhPPmvUXDYmiRJkiRJkgayeCRJkiRJkqSBHLbWU8N0NZQkSWvXYscK8+c9hH4NSxgm73L69Hq0cpY77vXfXZK6Z88jSZIkSZIkDWTPI0mSJEmSxkCfRqg4MffaYs8jSZIkSZIkDWTPozWuT5XrlbLca9pw/G5mVieKJEnSmrYSx5ordby6FntArNTcaM5LJamT4lGS04GLgAOAP6uqC7vIIemBbJtSP9k2pX6ybUr9ZNtcGYOKhnt7sYO+WIlhdn3qqDDM69l8+iEr9nyrPmwtyQHAW4BnAE8Ezk7yxNXOIemBbJtSP9k2pX6ybUr9ZNuURqOLOY9OAj5bVbdV1beBy4AzO8gh6YFsm1I/2TalfrJtSv1k25RGIFW1uk+YPAc4var+Xbv8AuDHquolC7ZbD6xvF48DPj3v7kcDX1mFuMMyz9LMs7Tl8jy2qo4YdYgVaJvj9r6uNvMsrW95YHLaZl/18d98ELOOzijy2jZHb9z+n+0tX99ojFPb7Nv/AfMM1qcsMJ55hmqbXcx5lEXW7VHBqqpNwKZFd5B8uKqmVzrYvjLP0syztB7l2a+22aPXAZhnOeZZXo8y7ff3Zh/16P1dlllHZ9zyLjCRbXMYY/7vtixf39jz780R61OePmWByc7TxbC17cAx85aPBu7oIIekB7JtSv1k25T6ybYp9ZNtUxqBLopHHwKOTfK4JA8Bng9c3UEOSQ9k25T6ybYp9ZNtU+on26Y0Aqs+bK2qdid5CfC3NJdOfGtVfWIvd9O3rr/mWZp5ltaLPCvQNnvxOuYxz9LMs7xeZFqh780+6sX7OySzjs645b3fBLfNYYztv9uQfH1jzL83V0Wf8vQpC0xwnlWfMFuSJEmSJEnjo4tha5IkSZIkSRoTFo8kSZIkSZI00NgVj5KcnuTTST6bZGPHWY5JckOSrUk+keSCLvO0mQ5I8pEk7+lBlsOSXJHkU+179OMd5/m19t/p40kuTfLQVX7+tybZmeTj89YdnuS6JJ9pfz9yNTOtBNvk8vrULsG2ucjzT2Tb7KvF3u++6utnymKSPDTJPyb5aJv1t7vOtJy+fTZqeePUfvfFOLX5fTGOnxOrzePa5fXps9tj2j2ef6THtGNVPEpyAPAW4BnAE4Gzkzyxw0i7gQ1V9QTgacCvdJwH4AJga8cZ5lwEvLeqfgQ4gQ5zJTkK+FVguqqeRDN53vNXOcZm4PQF6zYC11fVscD17fLYsE0OrU/tEmybC21mwtpmz21mz/e7r/r6mbKYbwGnVNUJwInA6Ume1nGm5fTts1HL28z4tN99MU5tfl+M4+fEqvG4dmh9+uz2mPaBNjPCY9qxKh4BJwGfrarbqurbwGXAmV2FqaodVXVze/temv+sR3WVJ8nRwLOAP+sqw7wsDwd+CrgYoKq+XVV3d5uKA4GDkhwIHAzcsZpPXlXvA762YPWZwJb29hbgrNXMtAJsk8voU7sE2+ZiJrRt9taA97uX+viZMkg1drWLD25/entVlL59Nmo449R+98U4tfl9MW6fEx3wuHYZffrs9ph2T6M+ph234tFRwBfmLW+nJx/oSdYBTwZu7DDGm4DfAL7bYYY5jwe+DLyt7db4Z0kO6SpMVX0R+EPgdmAH8PWqurarPPNMVdUOaL4ggMd0nGdv2SaX16d2CbbNYY1729QK69FnykDtUIJbgJ3AdVXV26z077NReoBxaPP7Ysw+J1abx7XL69Nnt8e0w1mxY9pxKx5lkXWdV8uTPAx4J/CyqrqnowxnADur6qYunn8RBwJPAf64qp4MfIMOh320YzvPBB4H/ABwSJJf6irPBLFNLp2jb+0SbJvSXuvLZ8pyquq+qjoROBo4KcmTus60mJ5+Nkr3G5c2vy/G5XOiIx7XLp2jb5/dHtOusnErHm0Hjpm3fDSr3BVsoSQPpmnMl1TVlR1GORl4dpJtNF0sT0nyFx3m2Q5sn3c24wqaxt2VpwP/VFVfrqrvAFcCP9Fhnjl3JjkSoP29s+M8e8s2ubS+tUuwbQ5r3NumVkjPPlOG0nbbn6W/c9P08bNRAsazze+LMfic6ILHtUvr22e3x7TDWbFj2nErHn0IODbJ45I8hGYCqqu7CpMkNGMst1bVG7rKAVBVr6yqo6tqHc378vdV1Vmls6q+BHwhyXHtqlOBT3aVh6b74NOSHNz+u51KPyZ6uxo4t719LvCuDrPsC9vkEvrWLttMts3hjHvb1Aro22fKUpIckeSw9vZBNAexn+o21eL6+NkowXi1+X0xTp8THfG4dgl9++z2mHZoK3ZMO1bFo6raDbwE+Fuaf4jLq+oTHUY6GXgBTdX1lvbnmR3m6ZuXApck+RjNFR1+v6sgbUX6CuBm4Faa//ubVjNDkkuBDwDHJdme5HzgQuC0JJ8BTmuXx4ZtcmzZNueZxLbZZwPe774ap8+UI4Eb2nb9IZq5TDq/jLImy5i1330xTm1+X/g5sQSPa8eSx7TzjPqYNlWdD+OUJEmSJElST41VzyNJkiRJkiStLotHkiRJkiRJGsjikSRJkiRJkgayeCRJkiRJkqSBLB5JkiRJkiRpIItHa0CS1yT59RXYz2FJ/uO85R9IcsX+7ldai2yXUj/ZNqV+sm1K/WTbXDssHukBkhy4xN2HAfc36Kq6o6qeM/pU0tpmu5T6ybYp9ZNtU+on2+Z4s3g0oZL8ZpJPJ/k74Lh23WyS6fb2o5Nsa2+fl+Qvk7wbuDbJw5Jcn+TmJLcmObPd7YXADyW5JckfJFmX5OPtPh6a5G3t9h9J8tPz9n1lkvcm+UyS/7rKb4XUG7ZLqZ9sm1I/2TalfrJtrk1LVf40ppI8FXg+8GSaf+ObgZuWediPAz9aVV9LUxH+uaq6J8mjgQ8muRrYCDypqk5sn2fdvMf/CkBVHZ/kR2g+GP5Fe9+JbZZvAZ9O8uaq+sIKvFRpbNgupX6ybUr9ZNuU+sm2uXZZPJpMPwlcVVXfBGgb43Kuq6qvtbcD/H6SnwK+CxwFTC3z+H8NvBmgqj6V5PPAXIO+vqq+3mb5JPBYwAattcZ2KfWTbVPqJ9um1E+2zTXK4tHkqkXW7eZ7QxUfuuC+b8y7fQ5wBPDUqvpO2+Vw4fYLZYn7vjXv9n34/05rl+1S6ifbptRPtk2pn2yba5BzHk2m9wE/l+SgJIcCP9uu3wY8tb291ORjjwB2to35p2mqtwD3Aocu8ZznALRdCH8Q+PQ+vwJp8tgupX6ybUr9ZNuU+sm2uUZZPJpAVXUz8A7gFuCd8H/Zu/c4S+r6zv+vtyABuQjKOCoQxwtONIxiMt5CLhNRdxQibH7qTxYMRJJJsmowmfziqJvVmI0hu96I5jYRHFwRNXhdMQYWbQ1RiaIoILq4ijCADgoIg0Yd/fz+qGo40/TpPt10n6rufj0fj/PoPlV1znmfc7q6qj71/X6Lf2lnvRb4vSSfBA6e4SnOAdYn+SzNSvrl9nm/A/xrkiuS/I8pj/kbYI8kl7evfUpV/QBJgOul1Feum1I/uW5K/eS6uXKlaroWZ5IkSZIkSZItjyRJkiRJkjQDi0eSJEmSJEkayuKRJEmSJEmShrJ4JEmSJEmSpKEsHknSEpVkZ5KHdZ1DWq6S/FOSk7vOIal7SV6V5O1d55Ckrqzo4lGSU5JcPKbXOjHJBYvwvGuSVJI9F/q5l4rZNuZJrkny1DHk2JBk+2K/jpa+9m/y+23xZ/L24Lk+T1XtV1VfW4yMUp9MWWe+mWRbkv0W+3Wr6hlVdfZiv849lWQiyW91nUNaaEl+Mcknk3w3yc1J/jXJ47vOJak7Hv92Z0UUj4YcqL15gZ57pD/eqjqnqp6+EK+51CzkmRoLNFpGfq0t/kzebhic6QZRuptfq6r9gCOBxwEv6zjPspFkj64zSFMlOQD4EPAm4H7AIcCfAj/oMpfUJ0n+Ocmrp5l+XHuyxf1JLZgVUTxqTT1Qe9G4XnglrLTL5T0ul/ehpWegEH1qkmuBjyY5P8mLpyz3xSTHt79Xkke0v29L8tftY25PckmShw887ulJvtKevf2bJB+3pYKWoqr6JvDPNEWku7W6mdqquF1PfjfJ1UluadeTDC6b5LXtvK8necbAY+987hGWfWiST7Tr3/9uX2fGEydJzk6yuf39kDbrf27vP6JtaZEkByX5UJKb2tf+UJJD2+X+HPgl4M2DJ8eS/EySC9vn+EqS5w687rYkf5vkw0nuAH51nl+HtJgeCVBV51bVj6vq+1V1QVV9sV0f/zXJm9rt2peTHD35wCT3TXJmkhuTXJ/kvw0WSZO8IMlV7fr0z0keMjDvZwfWnW8leflApr2SvK1dz69Msn4sn4Q03Dbg+ZPbtQHPB86pql3jj7TyrJRjyJVUPJrVLDta+yR5XZJvtBupi5PsA3yiXeTWdqftyQMbtDckuRl41TQ7szNtmKbL9oQkn01yW7v866cscmKSa5N8O8krpjzuU0lubTegb06yVzvv75K8dsrrfCDJH7a/PzjJe9qd1a8n+f2B5V6V5Lwkb09yG3DKkNwbgZcD/2/7+Xxh4Lk/2L7/ryb57YHH/FSSNya5ob29sZ22L/BPwINz964+s23MH5/kS+1OwluT7N2+1oYk25O8NMk3gbdO/a7a5QYP0p/ZPtft7Q7JH01ZdnOSHe3n/ZvTfS7SDH4FeBTwH4CzgZMmZyR5LM2Z1w8PeewJNGdlDwK+Cvx5+7iDgfNoWmrcH/gK8AuLE19aXGmKJs+g+Rsf1bHA44HHAs+lWb8mPZFmnTgY+O/AmdPshI+y7DuAf6NZx15Fs+M+m48DG9rffwX4WvsT4JeBf6mqotlfeyvwEOCnge8DbwaoqlcA/wK8aPLkWLu9vLDN9ACa/w1/k+RnB177P9H8j9gfGEsXfmmO/g/w4zRF1mckOWjK/CfSrDMHA68E3pvkfu28s4FdwCNoWio+HZgsBB9Ps2/668AqmvXn3Hbe/sD/Bj4CPLh9/EUDr/ks4J3AgcAHaddDqUPvp2mZ90uTE9p15VjgbUmOSfL59hjyuiSvGnxw7uoaems7/5R2+tATM5mm503ufrJl8lj41iRfS/IL7fTr2uOkWccTzPDj76nLDS0WJ3l4ko8m+U57nHxOkgMHHntNkj9Kc3L2u0neNXmc2M4/Nsll7fv4ZJLHTHnsS5N8EbgjK6CAZPGoNcKO1muBn6c54Lof8MfAT2h27gAObHfaPtXen9ygPYD2AG7gtWbbME3nDOCMqjoAeDjw7inzfxFYCxwN/Nckj2qn/xj4A5oN65Pb+f+5nfcOmqLO5BnYg2g2ru9Mci/gfwFfoDlYPRp4SZLBHe7jaA5IDwTOmS50VX0EeA3wrvbzeWw761xge/v+nw28JnedMXoF8CSas8qPBZ4A/JequoPmgOGGabr6zLYxP5HmYOHhNGey/svAvAfSfKcPATZN9z6mOBP4naraHzgC+OiU57ovzWd2KvDX0+zsSADvbzdEtyZ5/8D0V1XVHVX1feADwOFJDm/nPZ9mXfrhkOd8b1X9W3uW6RzalhnAM4Erq+q97by/Ar658G9JWlTvT3I7cB2wg+ZgcVSnV9WtVXUt8DHuWjcAvlFV/1BVP6Y54HwQsHrI80y7bJKfpilO/deq+mFVXUyzLZrNx4Ffare5v0xTkDqqnfcr7Xyq6jtV9Z6q+l5V3U6zX/Er06ukGJ0AACAASURBVD1h61jgmqp6a1XtqqrPAe+h2d5O+kBV/WtV/aSq/n2ErNJYVdVtNPu3BfwDcFOaE4+T6+cO4I1V9aOqehdNYfeYdv4zgJe029MdwBuA57WP+x3gL6rqqnab+BrgyDStj44FvllVr6uqf6+q26vqkoFYF1fVh9v/Af+TZj9V6ky7v/hu4DcGJj8X+HJVfQG4o513IHAM8Hu5qwX7T9OcmH8TTSH1SOCyBYr2ROCLNCdU3kFznPZ4muPek2hay842duGw4++phhaLgQB/QXPM+SjgMJoTPIOeC2wEHgo8hrZRRJKfA86i+Z9xf+DvgQ8m+amBx55A87keuBJaea2k4tHggdqtGWjp0hq6o9Xu1L0AOK2qrm+bzn6yqmbqc31DVb2pfa7vT/NaM22YpvMj4BFJDq6qnVX16Snz/7RtzvsFmoLPYwGq6tKq+nSb4xqaP/rJHc5/odkgT1aqnw18qi3IPB5YVVWvbneEv0az4X7eXS/Jp6rq/e2O59T3OFSSw2h2Bl7avv/LgLdw11naE4FXV9WOqrqJpiXFbGdwZ9uYv7mqrquqm2l2uk8YmPcT4JVV9YMR38ePgEcnOaCqbmn/VgbnvbrdkfkwsJOmqCdNdXxVHdjejh+Yft3kL+3/mHcDJ7X/h06g+fseZrAg9D1gcqP84CnPWzTFW2kpOb4t2m8AfobmpMiohq0bu82rqu+1vw7boR227IOBmwemwcA6N0xV/V+a7cSRNNviDwE3JFnLQPEoyX2S/H179vU2mlbPB2b4WEUPAZ44uN9Ds2194FzySV1rCzynVNWhNCfsHgy8sZ19fbs9m/SNdv5DgHsDNw78/f89zQld2vlnDMy7meYA8xCaA8v/O0Okqf9L9l4JrQ3Ue2cDzxlolfMb7TSqaqKqLm+P175IcwJ/8ljwROB/V9M19EftiYqFKh59vT2u/jHwLpp169Xt8dYFwA9pij3TGvX4e7ZicVV9taoubF/3JuD13P3ky19V1Q3tceL/4q4TTL8N/H1VXdK+/tk0Y649acpjr5vLsfBStpKKR4MHagdW1T9MmT/TjtbBwN7MvDGZaqadstk2TNM5labFzJeTfCbJsVPmT7tjnOSRacZG+Ga7w/ka2h3udoP7Tu4qpPwn7mpB9BCa7mGDn8fL2f1s7Hx3PCd3sm8fmPYNmo325PxvTJk325WoZtuYD2ad+nw3zfGs6/9D05LjG2nGjXnywLzvTKk6Tz1IkWZTU+6fTfO/6Gjge3VX68a5uBE4dPJO29rw0OGLS/1VVR+nGeNhstv1HcB9BhZ54NTHjMGNwP2SDOY4bMTHfpzm5M1eVXV9e/83aLqfTu7Eb6Y5EfHEalogT7Z6nuwyN/X/xnXAx6fs9+xXVb83sMzUx0i9VlVfpln3j2gnHTLZer7108ANNH//PwAOHvj7P6CqJnsTXEfTgnxw/dinqj7Zzns40hJSTWvXm4DjkjyMphHAOwCSPDHJx9IMQ/Jd4He56+TLfI5JR/Wtgd+/3+acOm2mY6RRj79nLBYneUCSd7bd2W4D3s7dTz4NO8H0EGDzlOPhw9j9OHJFnYhZScWj2cy0o/Vt4N+ZfmMybOdrpp2yOW+YqurqqjqBZkX4S+C8tqvdbP4W+DJweLvD+XLu2tmEpvr87Lap7hNpWltNZvz6lM9j/6p65mCsUeNPuX8DzU72/gPTfhq4fmD+Q6bMm+yeNt+d3cGd+MHnm+45dzsQSbLbgUhVfaaqjqP5Lt7P3bsQSgumLRb9BHgdM7c6msn5wLokx7dF1RfSzQG2tFDeCDwtyWQT+19vW+c8guZky1hV1TeAz9KMcbhXe1Lh10Z8+MeBF3HXGIoTwItpWtT+uJ22P82O9q1pxnSZ2mXvW8DDBu5/CHhkkucnuXd7e/xAl3ap99KMRbo5dw0OfxjNCc/J1vcPAH6//ft+Dk2XlA9X1Y3ABcDrkhyQ5F5pxj2ZbG3wd8DLJoemSDNeynPaeR8CHpjkJWnG29w/yRPH846le+RtNCceng9cMFCoeQdNN+rDquq+NH//k8eCMx2TznRi5o7252KeuJnp+HvQbMXiv6A51ntMeyx8ErsfC8/23H8+5Xj4PlV17sAyK+pEjMWjuwzd0aqqn9D0d3x9moGe90gzMPZP0VR5f8LuO22jvNacNkxJTkqyqs1yazv5xzM9prU/cBuwM8nPAINnHamqz7fv4S3AP1fV5HP/G3BbmkHA9mnf8xFJHj/627zTt4A1bfNDquo64JPAXyTZO83AY6dyV6unc4H/kmRVmoF+/ytNlXjyue6f5L5zzPDCJIe2O90vp2k+OcwXgJ9NcmSaAdNeNTmjPSg4Mcl9q+pHNJ/tKN+DdE+8DVjHXevBnFTVt4Hn0Iyn8h3g0TQHul7uWEtS2/T8bcCf0DRP/yHN9uFshozBNwYn0owt+B3gv9FsZ0ZZxz5Os62eLB5dTLND/omBZd4I7EOzM/1pmjETB51BcyLoliR/1bbsfTpNs/0baM6q/iXwU0hLx+00JzYvSXNVwE8DV9C0xAO4BDicZr34c+DZVfWddt5vAHsBXwJuoRmj80EAVfU+mvXhnW1LhCtour3QrjtPoyn+fhO4Gq9GqKXhbcBTabpanT0wfX+aHh//nuQJND1NJp0DPDXJc5PsmeT+7UkZmOHETLsNvp5mWIU9kryABW6xN8vx9+BysxWL96fpHn5rkkOA/28OMf4B+N229VaS7JtmAPL9Z33kclVVy/4GXENzxm7nwO19NINhXTyw3FqaM/Q30ez8fRQ4sp23D83O2/XAd2l26vZp5726fcytNH0gd3vedpmpr3UEzSDZt9BsnLbM8h7eTjMw4E7gSppueABraCqeew4sOwH8Vvv7L9O0PNpJM8bRq6fJ9iftczxnyvQH0xRyvtnm/DTw1Hbeq4C3j/j5359mZ/gW4HPttENpimg30zRH/N2B5femGdD3xvb2V8DeA/PPar+fW9uMu2WZ+pm03//LaHYgbqX5h3qfdt4GYPs0mV9BszNyHU2Fumj65e5Fs9N+C03h6DPALw57rva1n9r1OuBtad9odoIvXsDnuxfNAeWvdv3evHlbrjea4tGfdp3Dm7fleJtuX9ubt5V+a48BbwF+amDas2mG7Li9PfZ685Tjpl+iKcTe1h73nNxOP5imKHM78K/t8dbgsewzgK+3x1avozkRMnn8ecqUZR9BO2LKwLTtk8dQM7yfaY+/pznWuy9Nb5vt7XKfB57XzvtZ4FKaY+HLaIrP2wdeY7djtWmOKze2x3u30hyX/iOw/3SPXQm3tG9cktRD7RgqHwX+pqredg+e5z/Q7Bx8n+asywuBh9UKGeBPWmxty9ybaXamn07TrfnJ1bTwlbSA0lxO/Leq6he7ziJJK4Xd1iSpp9qCz0003XHecQ+f7sk0rfy+TdMc/3gLR9KCeiDNWd+dNC1mf6+qPt92dd45ze3KTtNKkiTNgS2PeiTJP9E0HZzqNVX1mnHnmYulnF2SJEmSNF7tiZSHTDPrd6qqqzEMNYTFI0mSJEmSJA21Z9cBRnHwwQfXmjVrxvZ6d9xxB/vuu+/YXm8uzDY/SznbpZde+u2qWjXGSCMb97o5H33+7mezlLPD8s+/lNbNvn0X5pmZeYYbJUvf181Vq1b15vOcqz79LcyFucdrWO4+r5sHHnhgPeIRj+g6xm769v33LQ+YaRQLut3sesTuUW4///M/X+P0sY99bKyvNxdmm5+lnA34bPVgPZzuNu51cz76/N3PZilnr1r++ZfSutm378I8MzPPcKNk6fu62afPc66WanZzj9ew3H1eNx/5yEcu1NtfMH37/vuWp8pMo1jI7aYDZkuSJEmSJGkoi0eSJEmSJEkayuKRJEmSJEmShrJ4JEmSJEmSpKEsHkmSJEmSJGkoi0eSJEmSJEkayuKRJEmSJGlJSXJYko8luSrJlUlOa6e/Ksn1SS5rb8/sOqu0HOzZdQBJkiRJkuZoF7C5qj6XZH/g0iQXtvPeUFWv7TCbtOxYPJIkSZIkLSlVdSNwY/v77UmuAg7pNpW0fFk8khbRmi3nz7rMto37jiGJpEGum1I/zbZuul6O9v/rmtOPGUMSqT+SrAEeB1wCHAW8KMlvAJ+laZ10yzSP2QRsAli1ahUTExPjijuSnTt39ipT3/KAmS6//ruzLvPQ++6xYHksHkmSJEkzSHIg8BbgCKCAFwBfAd4FrAGuAZ473QGqpMWVZD/gPcBLquq2JH8L/BnNuvpnwOto1tndVNVWYCvA2rVra8OGDWPLPIqJiQn6lKlvecBMp4x4MnSh8jhgtiRJkjSzM4CPVNXPAI8FrgK2ABdV1eHARe19SWOU5N40haNzquq9AFX1rar6cVX9BPgH4AldZpSWC4tHkiRJ0hBJDgB+GTgToKp+WFW3AscBZ7eLnQ0c301CaWVKEpr18qqqev3A9AcNLPYfgSvGnU1ajuy2JkmSJA33MOAm4K1JHgtcCpwGrG4H7KWqbkzygOkePDiuyurVq+/xeBib1+2adZnFGm+jj+OLjMLc4zXG3EcBzwcuT3JZO+3lwAlJjqTptnYN8DvjCCMtdxaPJEmSpOH2BH4OeHFVXZLkDObQRW1wXJX169fXfvvtd4/GnxhljItrTpz/88+kj+OLjMLc4zWu3FV1MZBpZn140V9cWoHstiZJkiQNtx3YXlWXtPfPoykmfWuye0z7c0dH+SRJWnQWjyRJkqQhquqbwHVJ1raTjga+BHwQOLmddjLwgQ7iSZI0FnZbkyRJkmb2YuCcJHsBXwN+k+Yk7LuTnApcCzynw3ySJC0qi0eSJEnSDKrqMmD9NLOOHncWSZK6YLc1SZIkSZIkDWXxSFphkpyVZEeSK6ZMf3GSryS5Msl/7yqfJEmSJKlfLB5JK882YOPghCS/ChwHPKaqfhZ4bQe5JEmSJEk9ZPFIWmGq6hPAzVMm/x5welX9oF3Gyw1LkiRJkgAHzJbUeCTwS0n+HPh34I+q6jPTLZhkE7AJYPXq1UxMTIwt5Hzs3Lmz9xmHWcrZod/5N6/bNesyfc4vSZIkjZPFI0nQ/C84CHgS8HiaSw8/rKpq6oJVtRXYCrB+/frasGHDOHPO2cTEBH3POMxSzg79zn/KlvNnXWbbxn17m1+SJEkap0XrtjbdoLxJ/keSLyf5YpL3JTlwsV5f0pxsB95bjX8DfgIc3HEmSZIkSVIPLOaYR9uYMigvcCFwRFU9Bvg/wMsW8fUlje79wFMAkjwS2Av4dqeJJEmSJEm9sGjFo+kG5a2qC6pqcqCJTwOHLtbrS5peknOBTwFrk2xPcipwFvCwtqXgO4GTp+uyJkmSJElaeboc8+gFwLuGzexyUN4+D5JqtvnpKlsfB+WtqhOGzDppbCEk3U2Ss4BjgR1VdUQ77X4028o1wDXAc6vqlq4ySpIkaWXqpHiU5BXALuCcYct0OShvnwd5Ndv8dJXNQXklzcE24M3A2wambQEuqqrTk2xp77+0g2ySJElawRZzzKNpJTmZ5szqiXaLkSSpMV13b+A44Oz297OB48caSpIkSWLMLY+SbKQ5Y/orVfW9cb62JElL0OqquhGgqm5M8oBhC87U3btv3YrNM7OVnGe27t59+2wkSVopFq141A7KuwE4OMl24JU0V1f7KeDCJACfrqrfXawMkiStFDN19+5bt2LzzGwl55mtu7ddvSVJ6saiFY+GDMp75mK9niRJy9C3kjyobXX0IGBH14EkSZK08ox9zCNJkjSyDwInt7+fDHygwyySJElaoSweSZLUA213708Ba5NsT3IqcDrwtCRXA09r70uSJEljNdYBsyVJ0vSGdPcGOHqsQSRJkqQpbHkkSZIkSZKkoSweSZIkSZIkaSiLR5IkSZIkSRrK4pEkSZIkSZKGsngkSZIkSZKkoSweSZIkSZIkaSiLR5IkSZIkSRpqz64DSJIkSYI1W87vOoIkSdOy5ZEkSZIkSZKGsngkSZIkSZKkoSweSZIkSZIkaSiLR9IKk+SsJDuSXDHNvD9KUkkO7iKbJEmSJKl/LB5JK882YOPUiUkOA54GXDvuQJIk9VmSa5JcnuSyJJ9tp90vyYVJrm5/HtR1TkmSFovFI2mFqapPADdPM+sNwB8DNd5EkiQtCb9aVUdW1fr2/hbgoqo6HLiovS9J0rK0Z9cBJHUvybOA66vqC0lmW3YTsAlg9erVTExMLH7Ae2Dnzp29zzjMUs4O/c6/ed2uWZfpc35JvXAcsKH9/WxgAnhpV2EkSVpMFo+kFS7JfYBXAE8fZfmq2gpsBVi/fn1t2LBh8cItgImJCfqecZilnB36nf+ULefPusy2jfv2Nr+ksSvggiQF/H27LVxdVTcCVNWNSR4w3QOnnnSZqTA9SmF7FItV+F6qRXVzj9dSzS1pZhaPJD0ceCgw2eroUOBzSZ5QVd/sNJkkSf1wVFXd0BaILkzy5VEfOPWky3777Te0MD1KYXsU15w4/fPfU30+KTATc4/XUs0taWaOeSStcFV1eVU9oKrWVNUaYDvwcxaOJElqVNUN7c8dwPuAJwDfSvIggPbnju4SSitPksOSfCzJVUmuTHJaO93B7KVFYPFIWmGSnAt8ClibZHuSU7vOJElSXyXZN8n+k7/TdPO+AvggcHK72MnAB7pJKK1Yu4DNVfUo4EnAC5M8GgezlxaF3dakFaaqTphl/poxRZEkaSlYDbyv7dq9J/COqvpIks8A725PwlwLPKfDjLtZM0L3t2tOP2YMSaTF0445Njnu2O1JrgIOwcHspUVh8UiSJEkaoqq+Bjx2munfAY4efyJJUyVZAzwOuIR5DGa/atWq3g3y3beBx/uWB8w07qsHWzySJEmSJC1JSfYD3gO8pKpua1sJzmpwMPu1a9f27grCfRt4vG95wEzjvnqwYx5JkiRJkpacJPemKRydU1XvbSc7mL20CCweSZIkSZKWlDRNjM4Erqqq1w/McjB7aREsWvEoyVlJdiS5YmCal02UJEmSJN1TRwHPB56S5LL29kzgdOBpSa4Gntbel3QPLWbLo23AxinTvGyiJEmSJOkeqaqLqypV9ZiqOrK9fbiqvlNVR1fV4e3Pm7vOKi0Hi1Y8qqpPAFNX1ONoLpdI+/P4xXp9SZIkSZIk3XPjvtraSJdNhN0vnbh69eqxXoKvj5f8m2S2+ekq27gvnyhJkiRJ0kIbd/FoZIOXTly/fv1YL53Yx0v+TTLb/HSVbdyXT5QkSZIkaaGN+2prXjZRkiRJkiRpCRl38cjLJkqSJEmSJC0hi1Y8SnIu8ClgbZLtSU7FyyZKkiRJkiQtKYs25lFVnTBk1tGL9ZqSJEmSJElaWOPutiZJkiRJkqQlxOKRJEk9l+QPklyZ5Iok5ybZu+tMkiRJWjksHkmS1GNJDgF+H1hfVUcAewDP6zaVJEmSVhKLR5Ik9d+ewD5J9gTuA9zQcR5JkiStIIs2YLYkSbrnqur6JK8FrgW+D1xQVRdMXS7JJmATwOrVq5mYmLhz3s6dO3e73zXzzGwl59m8bldvskiSpLtYPJIkqceSHAQcBzwUuBX4xyQnVdXbB5erqq3AVoD169fXhg0b7pw3MTHB4P2umWdmKznPKVvOn3H+to379uqzkSRppbDbmrTCJDkryY4kVwxM+x9Jvpzki0nel+TALjNK2s1Tga9X1U1V9SPgvcAvdJxJkiRJK4jFI2nl2QZsnDLtQuCIqnoM8H+Al407lKShrgWelOQ+SQIcDVzVcSZJkiStIBaPpBWmqj4B3Dxl2gVVNTnQxKeBQ8ceTNK0quoS4Dzgc8DlNNvurZ2GkiRJ0orimEeSpnoB8K5hM2calLePlvLgqks5O/Q7/2yD8kK/8lfVK4FXdp1DkiRJK5PFI0l3SvIKYBdwzrBlZhqUt4/6NvDsXCzl7NDv/LMNygsOzCtJkiRNsngkCYAkJwPHAkdXVXWdR5IkSZLUDxaPJJFkI/BS4Feq6ntd55EkSZIk9YcDZksrTJJzgU8Ba5NsT3Iq8GZgf+DCJJcl+btOQ0qSJEmSesOWR9IKU1UnTDP5zLEHkSRJkiQtCbY8kiRJkiRJ0lAWjyRJkiRJkjSUxSNJkiRpFkn2SPL5JB9q7z80ySVJrk7yriR7dZ1RkqTFYvFIkiRJmt1pwFUD9/8SeENVHQ7cApzaSSpJksbA4pEkSZI0gySHAscAb2nvB3gKcF67yNnA8d2kkyRp8Xm1NUmSJGlmbwT+GNi/vX9/4Naq2tXe3w4cMt0Dk2wCNgGsXr2anTt3MjExMe2LbF63a9rpi2FYhpnMlL3PzD1eSzW3pJlZPJIkSZKGSHIssKOqLk2yYXLyNIvWdI+vqq3AVoD169fXfvvtx4YNG6ZblFO2nH+P847qmhOnzzCTiYmJodn7zNzjtVRzS5qZxSNJkiRpuKOAZyV5JrA3cABNS6QDk+zZtj46FLihw4ySJC0qxzySJEmShqiql1XVoVW1Bnge8NGqOhH4GPDsdrGTgQ90FFGSpEVn8UiSJEmau5cCf5jkqzRjIJ3ZcR5JkhaN3dYkSdK8rBlhfJZrTj9mDEk0jN/RwqqqCWCi/f1rwBO6zCNJ0rjY8kiSJEmSJElDdVI8SvIHSa5MckWSc5Ps3UUOSZIkSZIkzWzsxaMkhwC/D6yvqiOAPWgGH5QkSZIkSVLPdNVtbU9gnyR7AvfBS5tKkiRJkiT10tgHzK6q65O8FrgW+D5wQVVdMHW5JJuATQCrV69mYmJibBl37tw51tebC7PNT1fZNq/bNesyff7cJEmSJEkae/EoyUHAccBDgVuBf0xyUlW9fXC5qtoKbAVYv359bdiwYWwZJyYmGOfrzYXZ5qerbKeMcJWbbRv37e3nJkmSJPVRkrOAY4Ed7XAoJHkV8NvATe1iL6+qD3eTUFpeuui29lTg61V1U1X9CHgv8Asd5JAkSZIkLU3bgI3TTH9DVR3Z3iwcSQuki+LRtcCTktwnSYCjgas6yCGtSEnOSrIjyRUD0+6X5MIkV7c/D+oyoyRJkjSTqvoEcHPXOaSVoosxjy5Jch7wOWAX8Hna7mmSxmIb8GbgbQPTtgAXVdXpSba091/aQTZJkiTpnnhRkt8APgtsrqpbpltocIzdVatW9W4M0r6Ni9q3PGCmcY+vO/biEUBVvRJ4ZRevLa10VfWJJGumTD4O2ND+fjYwgcUjSZIkLS1/C/wZUO3P1wEvmG7BwTF2165dO9YxdkfRt/Fk+5YHzDTu8XU7KR5J6p3VVXUjQFXdmOQBwxbs8kqI89HHMxKjWsrZod/5vRKiJEnLT1V9a/L3JP8AfKjDONKyYvFI0px0eSXE+ejjGYlRLeXs0O/8XglRkqTlJ8mDJk+IAv8RuGKm5SWNbqQBs5OcluSANM5M8rkkT1/scJLG5ltJHgTNRhfY0XEeSZIkaagk5wKfAtYm2Z7kVOC/J7k8yReBXwX+oNOQ0jIyasujF1TVGUn+A7AK+E3grcAFi5ZM0jh9EDgZOL39+YFu40iSJEnDVdUJ00w+c+xBpBVipJZHQNqfzwTeWlVfGJgmaQkZcpbmdOBpSa4GntbelyRJkiRp5JZHlya5AHgo8LIk+wM/WbxYkhbLkLM0AEePNYgkSZIkaUkYtXh0KnAk8LWq+l6S+9N0XZMkSZIkSdIyNmq3tQur6nNVdStAVX0HeMPixZIkSZOSHJjkvCRfTnJVkid3nUmSJEkrx4wtj5LsDdwHODjJQdw1ztEBwIMXOZskSWqcAXykqp6dZC+abbMkSZI0FrN1W/sd4CU0haJLuat4dBvw14uYS5IkAUkOAH4ZOAWgqn4I/LDLTJIkSVpZZiweVdUZwBlJXlxVbxpTJkmSdJeHATcBb03yWJqTOadV1R2DCyXZBGwCWL16NRMTE3fO27lz5273F8rmdbtmXWa6112sPPM1lzyXX//dWZdZd8h9x5ZnNvP9jhYrz2xmy9u3vx1JklaKkQbMrqo3JfkFYM3gY6rqbYuUS5IkNfYEfg54cVVdkuQMYAvwJ4MLVdVWYCvA+vXra8OGDXfOm5iYYPD+Qjlly/mzLnPNiXd/3cXKM19zyTPf97xYeWazEHnH+X3Nlnfbxn179bcjSdJKMVLxKMn/BB4OXAb8uJ1cgMUjSZIW13Zge1Vd0t4/j6Z4JEmSJI3FSMUjYD3w6KqqxQwjSZJ2V1XfTHJdkrVV9RXgaOBLXeeSJEnSyjFq8egK4IHAjYuYRZIkTe/FwDntlda+Bvxmx3kkSZK0goxaPDoY+FKSfwN+MDmxqp61KKkkSdKdquoymlbAkiRJ0tiNWjx61WKGkCRJkiRJUj+NerW1jy92EEmSJEmSJPXPqFdbu53m6moAewH3Bu6oqgMWK5gkSZIkSZK6N2rLo/0H7yc5HnjCoiSSJEmSJElSb9xrPg+qqvcDT1ngLJIkSVKvJNk7yb8l+UKSK5P8aTv9oUkuSXJ1kne1V0OUJGlZGrXb2q8P3L0XzRVfasjikiRJ0nLxA+ApVbUzyb2Bi5P8E/CHwBuq6p1J/g44FfjbLoNKkrRYRr3a2q8N/L4LuAY4bsHTSJIkST1SVQXsbO/eu70VTSv8/9ROP5vm6sQWjyRJy9KoYx795mIHkSRJkvooyR7ApcAjgL8G/i9wa1XtahfZDhwy5LGbgE0Aq1evZufOnUxMTEz7OpvX7Zp2+mJ40zkfmHH+ukPue7dpM2XvM3OP11LNLWlmo3ZbOxR4E3AUzZmWi4HTqmr7ImaTJEmSOldVPwaOTHIg8D7gUdMtNuSxW4GtAOvXr6/99tuPDRs2TPs6p2w5f0HyLoRrTtxwt2kTExNDs/eZucdrqeaWNLNRB8x+K/BB4ME0Z1X+VztN0jKS5A/awUCvSHJukr27ziRJUl9U1a3ABPAk4MAkkydiDwVu6CqXJEmLbdTi0aqqemtVFElNDgAAGiZJREFU7Wpv24BVi5hL0pglOQT4fWB9VR0B7AE8r9tUkiR1K8mqtsURSfYBngpcBXwMeHa72MnAzP3AJElawkYtHn07yUlJ9mhvJwHfme+LJjkwyXlJvpzkqiRPnu9zSVpQewL7tGdS74NnUSVJehDwsSRfBD4DXFhVHwJeCvxhkq8C9wfO7DCjJEmLatSrrb0AeDPwBpr+3J8E7skg2mcAH6mqZyfZi+YgVVKHqur6JK8FrgW+D1xQVRdMXW7qwJ99HxBxKQ/auJSzQ7/zjzIobZ/zSxqfqvoi8Lhppn8NeML4E0mSNH6jFo/+DDi5qm4BSHI/4LU0RaU5SXIA8MvAKQBV9UPgh3N9HkkLK8lBwHHAQ4FbgX9MclJVvX1wuakDf/Z9QMSlPGjjUs4O/c4/yqC02zbu29v8kiRJ0jiNWjx6zGThCKCqbk5ytzMwI3oYcBPw1iSPpbns6WlVdcfgQl22bujz2WazzU9X2ZZY64anAl+vqpsAkrwX+AXg7TM+SpIkSZK0rI1aPLpXkoOmtDwa9bHTvebPAS+uqkuSnAFsAf5kcKEuWzf0+Wy52eanq2xLrHXDtcCTktyHptva0cBnu40kSZIkSeraqAWg1wGfTHIezZhHzwX+fJ6vuR3YXlWXtPfPoykeSepQW8w9D/gcsAv4PG0BV5IkSZK0co1UPKqqtyX5LPAUIMCvV9WX5vOCVfXNJNclWVtVX6Fp3TCv55K0sKrqlcAru84hSZIkSeqPkbuetcWihSryvBg4p73S2te4Z1dukyRJkiRJ0iKZ77hF90hVXQas7+K1JUmSJEmSNLp7dR1AkiRJkiRJ/WXxSJIkSZIkSUNZPJIkSZIkSdJQFo8kSZIkSZI0lMUjSZIkSZIkDWXxSJIkSZIkSUNZPJIkSZIkLSlJzkqyI8kVA9Pul+TCJFe3Pw/qMqO0nFg8kiRJkiQtNduAjVOmbQEuqqrDgYva+5IWgMUjSZIkSdKSUlWfAG6eMvk44Oz297OB48caSlrG9uw6gCRJkiRJC2B1Vd0IUFU3JnnAsAWTbAI2AaxatYqJiYnxJBzRzp07e5Wpb3nATJvX7Zp1mYXMY/FIkiRJkrSiVNVWYCvA2rVra8OGDd0GmmJiYoI+ZepbHjDTKVvOn3WZbRv3XbA8dluTJEmSJC0H30ryIID2546O80jLhsUjSZIkSdJy8EHg5Pb3k4EPdJhFWlYsHkmSJEmSlpQk5wKfAtYm2Z7kVOB04GlJrgae1t6XtAAc80iSpCUgyR7AZ4Hrq+rYrvNIktSlqjphyKyjxxpEWiFseSRJ0tJwGnBV1yEkSZK08lg8kiSp55IcChwDvKXrLJIkSVp57LYm6U5JDqQ5OD0CKOAFVfWpblNJAt4I/DGw/7AFkmwCNgGsXr2aiYmJO+ft3Llzt/sLZfO6XbMu86Zz7j5W6ep97pq+7pD7LniuuZr8fC6//ruzLrt53ezPN8pnPdNrrd5ntOcYxSjf0WyvtePm7077PQ5aqO9xtryL9bcsSZJmZvFI0qAzgI9U1bOT7AXcp+tA0kqX5FhgR1VdmmTDsOWqaiuwFWD9+vW1YcNdi05MTDB4f6GcsuX8eT1u87pdvO7yZhfkmhM3LGCi+Zn8fOb7fqYa5T3N9Fqb1+3iuQv0fY3ynmbL+6ZzPnDn9zXf5xjVbHm3bdx3Uf6WJUnSzCweSQIgyQHALwOnAFTVD4EfdplJEgBHAc9K8kxgb+CAJG+vqpM6ziVJkqQVwuKRpEkPA24C3prkscClwGlVdcfgQjN1jemjpdzFYSlnh37nH6UrT1/yV9XLgJcBtC2P/sjCkSRJksbJ4pGkSXsCPwe8uKouSXIGsAX4k8GFZuoa00eL1V1nHJZyduh3/lG68tg9RpIkSWp4tTVJk7YD26vqkvb+eTTFJEk9UVUTVXVs1zkkSZK0slg8kgRAVX0TuC7J2nbS0cCXOowkSVLnkhyW5GNJrkpyZZLT2un3S3Jhkqvbnwd1nVWSpMVi8UjSoBcD5yT5InAk8JqO80iS1LVdwOaqehTwJOCFSR5N07X7oqo6HLiovS9J0rLkmEeS7lRVlwHru84hSVJfVNWNwI3t77cnuQo4BDgO2NAudjYwAby0g4iSJC06i0eSJEnSCJKsAR4HXAKsbgtLVNWNSR4w5DG7XaV0pis5jnIlyHF50zkfuNu01fvsPn3dIfcdZ6R568vVM+fK3JL6pLPiUZI9gM8C1zv4pyRJkvosyX7Ae4CXVNVtSUZ63NSrlO63335Dr+Q4ypUgu7R53S5ed/ldhw/XnLihuzBz0Oerf87E3JL6pMsxj04Drurw9SVJkqRZJbk3TeHonKp6bzv5W0ke1M5/ELCjq3ySJC22TopHSQ4FjgHe0sXrS5IkSaNI08ToTOCqqnr9wKwPAie3v58M3L2flyRJy0RX3dbeCPwxsP+wBab2Dx9nv9k+99M12/x0lW2UsQv6/LlJkiSOAp4PXJ7ksnbay4HTgXcnORW4FnhOR/kkSVp0Yy8eJTkW2FFVlybZMGy5qf3Dx9lvts/9dM02P11lG2Xsgm0b9+3t5yZJ0kpXVRcDwwY4OnqcWSRJ6koX3daOAp6V5BrgncBTkry9gxySJEmSJEmaxdiLR1X1sqo6tKrWAM8DPlpVJ407hyRJkiRJkmbX5dXWJEmSJEmS1HNdDZgNQFVNABNdZpAkSZIkSdJwtjySJEmSJEnSUBaPJEmSJEmSNJTFI0mSJEmSJA3V6ZhHkiSpn9ZsOb83r3PN6ccsyPMMs3ndLk5ZwPfbp89uIZ5n87qFyTLK9yhJkvrJlkeSJEmSJEkayuKRJEmSJEmShrJ4JEmSJEmSpKEsHkmSJEmSJGkoi0eSdpNkjySfT/KhrrNIkiRJkrpn8UjSVKcBV3UdQpIkSZLUDxaPJN0pyaHAMcBbus4iSZIkSeqHPbsOIKlX3gj8MbD/sAWSbAI2AaxevZqJiYnxJJunnTt39j7jMEs5O/Q7/+Z1u2Zdps/5JUmSpHGyeCQJgCTHAjuq6tIkG4YtV1Vbga0A69evrw0bhi7aCxMTE/Q94zBLOTv0O/8pW86fdZltG/ftbX5JkiRpnOy2JmnSUcCzklwDvBN4SpK3dxtJkiRJktQ1i0eSAKiql1XVoVW1Bnge8NGqOqnjWJIkSZKkjlk8kiRJkiRJ0lCOeSTpbqpqApjoOIYkSZIkqQcsHkmSJEmSlo12DM/bgR8Du6pqfbeJpKXP4pEkSZIkabn51ar6dtchpOXCMY8kSZIkSZI0lC2PJEmSJEnLSQEXJCng76tq69QFkmwCNgGsWrWKiYmJ8Sacxc6dO3uVqW95wEyb1+2adZmFzGPxSJKkHktyGPA24IHAT4CtVXVGt6kkCdZsOX/WZa45/ZgxJJHu5qiquiHJA4ALk3y5qj4xuEBbUNoKsHbt2tqwYUMHMYebmJigT5n6lgfMdMoI/4O3bdx3wfLYbU2SpH7bBWyuqkcBTwJemOTRHWeSJKm3quqG9ucO4H3AE7pNJC19Fo8kSeqxqrqxqj7X/n47cBVwSLepJEnqpyT7Jtl/8nfg6cAV3aaSlj67rUmStEQkWQM8Drhkmnl3jt2wevXq3fq3z6e/+yj96Odr9T5ze/5Rst+TvHPNs9hW7wNvOucDsy63ed0YwrBwn89CfI99HN9CUu+sBt6XBJrj3XdU1Ue6jSQtfRaPJElaApLsB7wHeElV3TZ1/uDYDevXr99t7Ib59L8fpR/9fG1et4vXXT76Lsg1J26YdZl7kneueRbbcs2zEN/jQo7dMKokZwHHAjuq6oh22v2AdwFrgGuA51bVLWMNJmlaVfU14LFd55CWG7utSZLUc0nuTVM4Oqeq3tt1HmmF2QZsnDJtC3BRVR0OXNTelyRp2bJ4JElSj6Vpd38mcFVVvb7rPNJK016h6eYpk48Dzm5/Pxs4fqyhJEkas7G3ifaSw5IkzclRwPOBy5Nc1k57eVV9uMNM0kq3uqpuhGZQ+/Zy4NOaOh7ZTOM29WnsrenMZ/yrUcbvWnfIfecbaSRLdawsc0vqky461E9ecvhz7Sj4lya5sKq+1EEWSZJ6raouBtJ1DknzM3U8sv3222/ouE2LOdbYQlis8bhGGQ/rnpjPuG99YG5JfTL2bmtecliSJElL3LeSPAig/bmj4zySJC2qTi/lMd9LDi+Wy6//LjDzJXIXu1ntbBa6Gejke57JqO+5z01U55ptoT6XUZp29/lzkyRJ0/ogcDJwevtz9r5ZkiQtYZ0Vj+7JJYcXy2RT4Zma5C52s9rZLHQz0FGaR4/6nvvcRHWu2Rbqcxnlebq47LAkSRpNknOBDcDBSbYDr6QpGr07yanAtcBzuksoSdLi66R45CWHJUmStBRU1QlDZh091iCSJHVo7GMeeclhSZIkSZKkpWPsxSPuuuTwU5Jc1t6e2UEOSZIkSZIkzWLs3da85LDUT0kOA94GPBD4CbC1qs7oNpUkSdLs1swwzuTmdbs4Zcv5XHP6MWNMJEnLS6dXW5PUK7uAzVX1uST7A5cmubCqvtR1MEmSJElSd7rotiaph6rqxqr6XPv77cBVwCHdppIkSZIkdc2WR5LuJska4HHAJdPM2wRsAli9ejUTExPjjDZnO3fu7H3GYZZyduh3/s3rds26TJ/zS5IkSeNk8UjSbpLsB7wHeElV3TZ1flVtBbYCrF+/vjZs2DDegHM0MTFB3zMOs5SzQ7/znzLD2BiTtm3ct7f5JUmSpHGy25qkOyW5N03h6Jyqem/XeSRJkiRJ3bN4JAmAJAHOBK6qqtd3nUeSJEmS1A8WjyRNOgp4PvCUJJe1t2d2HUqSJEmS1C3HPJIEQFVdDKTrHJIkSZKkfrF4JEnSCrNmhAHD+2Sp5dX0/B4lSVq67LYmSZIkSZKkoSweSZIkSZIkaSi7rUmSJEnqLbs8SivbsP8Bm9ft4pR23jWnHzPOSCvSsikezbZRGecf00Js4EbJ26cN6Zot5++28s7HQr3npfbZSZIkSZLUZ3ZbkyRJkiRJ0lDLpuWRJEmSpKXHFuGS1H+2PJIkSZIkSdJQtjyStKjGdTZxqQ2SN8rnMts4Yn16z9O9n6n5FyLvQo17JkmSJGl0tjySJEmSJEnSULY8kiRJkrTs2RpakubPlkeSJEmSJEkaypZHkiRJkiQNMc4xFydfa9jYl6O8zkLlHXye6fL0qZXduL6j2V5ntjFLlzJbHkmSJEmSJGkoi0eSJEmSJEkayuKRJEmSJEmShrJ4JEmSJEmSpKEsHkmSJEmSJGkor7YmSZIkSRpqtitM9emqW5IWRyctj5JsTPKVJF9NsqWLDJLuznVT6ifXTamfXDelfnLdlBbe2ItHSfYA/hp4BvBo4IQkjx53Dkm7c92U+sl1U+on102pn1w3pcXRRcujJwBfraqvVdUPgXcCx3WQQ9LuXDelfnLdlPrJdVPqJ9dNaRGkqsb7gsmzgY1V9Vvt/ecDT6yqF01ZbhOwqb27FvjKGGMeDHx7jK83F2abn6Wc7SFVtWqxQyyRdXM++vzdz2YpZ4fln38prZt9+y7MMzPzDDdKlr6vm9+hP5/nXPXpb2EuzD1ew3L3ed08ArhisbPNUd++/77lATONYsG2m10MmJ1ppt2tglVVW4Gtix/n7pJ8tqrWd/HaszHb/JhtJL1fN+ejR5/vnC3l7GD+BXSP180evRfAPLMxz3B9ysI8182evYc5WarZzT1ePcg953WzB5nvpm+Z+pYHzDSKhczTRbe17cBhA/cPBW7oIIek3bluSv3kuin1k+um1E+um9Ii6KJ49Bng8CQPTbIX8Dzggx3kkLQ7102pn1w3pX5y3ZT6yXVTWgRj77ZWVbuSvAj4Z2AP4KyqunLcOWbR5y45Zpsfs81iiayb89GLz3eelnJ2MP+CWKB1sxfvZYB5Zmae4XqT5R6sm715D/OwVLObe7w6zT3PdbOPn3XfMvUtD5hpFAuWZ+wDZkuSJEmSJGnp6KLbmiRJkiRJkpYIi0eSJEmSJEkayuJRK8lhST6W5KokVyY5retMUyXZI8nnk3yo6yyDkhyY5LwkX24/vyd3nWlSkj9ov88rkpybZO8Os5yVZEeSKwam3S/JhUmubn8e1FW+5STJ2iSXDdxuS/KSrnPNRZ/+ducqyWlt7iuXwue+3NfNJBuTfCXJV5Ns6TBHL7ezfdq29m172vX/oeW4bvZlfZyLvq67o+rTOj4Xfft/MKqu/2/MR5/Wyz6vb31bl/q2jvThb3+xt5sWj+6yC9hcVY8CngS8MMmjO8401WnAVV2HmMYZwEeq6meAx9KTjEkOAX4fWF9VR9AMmPe8DiNtAzZOmbYFuKiqDgcuau/rHqqqr1TVkVV1JPDzwPeA93Uca2Q9/NsdWZIjgN8GnkDz/+DYJId3m2pW21im62aSPYC/Bp4BPBo4ocNtW1+3s33atvZme9qT/0PbWEbrZs/Wx7no67o7qj6t43Px/7d3r6GW1WUcx78/msJrCU5KajYWZUGFN6Kyhsx60c3JKDAsNHoRZUEvIrq8qFdhKREIBaGZkFh5iQzCS0YZhVKOhuYFy7xMmkqWioKXfHqxl3rmNPvM2cPMrGfv8/28Oeusdfbazz77//v/Dw9r7dNmPlitJvPGTBrmsnPeumWpTUYajf0fsgvXTZtHg6q6r6o2D9uPMhl8B49b1fOSHAK8Dzh77FqWSvJiYCNwDkBVPVlV/xm3qq2sA/ZMsg7YC7h3rEKq6mrgoWW7NwHnDdvnAR/crUWtDccDf6uqu8YuZEZtxu6MXgdcU1WPV9XTwG+BE0euaUULns03AX+tqjuq6kngx0xe227XcZ3ttLY2XU9HnYcWMJtt8jiLjtldrU4Zn0XT+WC15u3vl1a57Jq3bllqmpHRx/6uXjdtHm1Dkg3AkcC141ayle8AXwSeGbuQZV4JPAicO1zGeHaSvccuCqCq/gGcCdwN3Ac8XFVXjFvV/zmwqu6DyWIBHDByPYvoJOCCsYuYxZyM3WluAjYm2T/JXsB7gZePXNOOWJRsHgzcs+T7LfT4I3QDPdbZTmtrq/W08Tw0z9lsmcdZNMruanXK+CxazQer1XjeWEnbXDbLW7cstcpI87G/09ZNm0fLJNkHuBj4fFU9MnY9AEneDzxQVdeNXcs2rAOOAr5XVUcCj9HkEvLhfs5NwGHAQcDeST42blXanZK8CDgBuHDsWmYxz2O3qm4BvglcCVwG/JnJJdgaR7axr3Z7FUt0WWcbrq2t1tN5nocaa5fHWXTJ7mo1zPgsWs0HqzWn80bLXHbKW9MstcrInI79mdk8WiLJC5mE9PyqumTsepY4FjghyZ1MLqV8Z5IfjVvSc7YAW6rq2Y74RUyC3MG7gL9X1YNV9RRwCfDWkWta7v4kLwMYvj4wcj2L5j3A5qq6f+xCZjQPY3eqqjqnqo6qqo1MLp29feyadsCiZHMLW1/5dQgj3kLQbJ3ttrZ2W0+7zkPznM1WeZxFs+yuVreMz6LbfLBaXeeNlbTLZcO8dcxSt4x0Hvs7bd20eTRIEib3TN5SVd8eu56lqurLVXVIVW1gcgvOr6uqRSezqv4J3JPk8GHX8cDNI5a01N3Am5PsNby/x9PrQ94ALgVOGbZPAX4+Yi2L6KPM2S1rg3kYu1MlOWD4eijwIebzPViUbP4ReHWSw4Yr8U5i8tp2u27rbLe1teF62nUemudstsnjLLpld7W6ZXwWDeeD1eo6b6ykVS475q1jlhpmpPPY32nr5rqdUs5iOBb4OHBjkhuGfV+pql+OWNO8+Bxw/jDh3gF8YuR6AKiqa5NcBGxmctvM9cD3x6onyQXAO4D1SbYAXwNOB36a5JNMJp2PjFXfohk+b+fdwKfGrmVW3cbuDrg4yf7AU8BpVfXvsQtaySJns6qeTvJZ4HIm//njB1X1l5HKcZ3dvjbraYd5aNGy2SyPszC742gzH6xWh3ljVg1zad5Wr01Guoz9Xb1upmr0WzolSZIkSZLUlLetSZIkSZIkaSqbR5IkSZIkSZrK5pEkSZIkSZKmsnkkSZIkSZKkqWweSZIkSZIkaSqbR2tAkq8n+cJOOM9+ST6z5PuDhn9JKGlG5lLqyWxKPZlNqSezuXbYPNJWkqxb4fB+wHOBrqp7q+rDu74qaW0zl1JPZlPqyWxKPZnN+WbzaEEl+WqS25L8Cjh82PebJMcM2+uT3Dlsn5rkwiS/AK5Isk+Sq5JsTnJjkk3DaU8HXpXkhiRnJNmQ5KbhHHskOXf4+euTHLfk3JckuSzJ7Um+tZt/FVIb5lLqyWxKPZlNqSezuTat1PnTnEpyNHAScCST93gzcN12HvYW4I1V9VAmHeETq+qRJOuBa5JcCnwJeH1VHTE8z4Yljz8NoKrekOS1TCaG1wzHjhhqeQK4LclZVXXPTnip0twwl1JPZlPqyWxKPZnNtcvm0WJ6O/CzqnocYAjj9lxZVQ8N2wG+kWQj8AxwMHDgdh7/NuAsgKq6NcldwLOBvqqqHh5quRl4BWCgtdaYS6knsyn1ZDalnszmGmXzaHHVNvY9zfO3Ku6x7NhjS7ZPBl4KHF1VTw2XHC7/+eWywrEnlmz/F8ed1i5zKfVkNqWezKbUk9lcg/zMo8V0NXBikj2T7At8YNh/J3D0sL3Sh4+9BHhgCPNxTLq3AI8C+67wnCcDDJcQHgrctsOvQFo85lLqyWxKPZlNqSezuUbZPFpAVbUZ+AlwA3Ax8Lvh0JnAp5P8AVi/winOB45J8icmIb11OO+/gN8nuSnJGcse813gBUluHJ771Kp6AkmAuZS6MptST2ZT6slsrl2p2tYVZ5IkSZIkSZJXHkmSJEmSJGkFNo8kSZIkSZI0lc0jSZIkSZIkTWXzSJIkSZIkSVPZPJIkSZIkSdJUNo8kSZIkSZI0lc0jSZIkSZIkTfU/lXojQShr2EkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x103964828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, arr = plt.subplots(2,5, figsize=(20,10))\n",
    "for i, label in enumerate(np.unique(df[\"event_label\"])):\n",
    "    df[df[\"event_label\"] == label][\"duration\"].hist(bins=20, ax=arr[int(i/5), i%5])\n",
    "    arr[int(i/5), i%5].set_title(label)\n",
    "    arr[int(i/5), i%5].set_xlabel(\"duration\")\n",
    "    if i%5 == 0:\n",
    "        arr[int(i/5), i%5].set_ylabel(\"counts\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean duration of events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean duration of events\n",
      "Alarm_bell_ringing             : 1.534518\n",
      "Blender                        : 5.354750\n",
      "Cat                            : 0.813392\n",
      "Dishes                         : 0.559590\n",
      "Dog                            : 1.026252\n",
      "Electric_shaver_toothbrush     : 7.415429\n",
      "Frying                         : 9.336375\n",
      "Running_water                  : 5.611053\n",
      "Speech                         : 1.514981\n",
      "Vacuum_cleaner                 : 8.655472\n"
     ]
    }
   ],
   "source": [
    "means = []\n",
    "labels = np.unique(df[\"event_label\"])\n",
    "print(\"Mean duration of events\")\n",
    "for label in labels:\n",
    "    mean_duration = df[df[\"event_label\"] == label][\"duration\"].mean()\n",
    "    means.append(mean_duration)\n",
    "    print(\"{:30} : {:3f}\".format(label, mean_duration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median duration of events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median duration of events\n",
      "Alarm_bell_ringing             : 0.575500\n",
      "Blender                        : 4.587500\n",
      "Cat                            : 0.640000\n",
      "Dishes                         : 0.418500\n",
      "Dog                            : 0.573000\n",
      "Electric_shaver_toothbrush     : 8.795500\n",
      "Frying                         : 10.000000\n",
      "Running_water                  : 5.529500\n",
      "Speech                         : 1.175000\n",
      "Vacuum_cleaner                 : 9.985500\n"
     ]
    }
   ],
   "source": [
    "medians = []\n",
    "labels = np.unique(df[\"event_label\"])\n",
    "print(\"Median duration of events\")\n",
    "for label in labels:\n",
    "    mean_duration = df[df[\"event_label\"] == label][\"duration\"].median()\n",
    "    medians.append(mean_duration)\n",
    "    print(\"{:30} : {:3f}\".format(label, mean_duration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Durations, mean over events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean duration on mean events (macro)\n",
      "4.182181101030015\n"
     ]
    }
   ],
   "source": [
    "print(\"mean duration on mean events (macro)\")\n",
    "print(np.mean(means))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "median duration on median events (macro)\n",
      "2.88125\n"
     ]
    }
   ],
   "source": [
    "print(\"median duration on median events (macro)\")\n",
    "print(np.median(medians))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Durations, overall data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median duration over all events (micro)\n",
      "1.0260000000000002\n"
     ]
    }
   ],
   "source": [
    "print(\"Median duration over all events (micro)\")\n",
    "print(df[\"duration\"].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean duration over all events (micro)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.414638136511371"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"mean duration over all events (micro)\")\n",
    "df[\"duration\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
